View source: R/local_coordinate_coding.R

local_coordinate_coding | R Documentation |

An implementation of Local Coordinate Coding (LCC), a data transformation technique. Given input data, this transforms each point to be expressed as a linear combination of a few points in the dataset; once an LCC model is trained, it can be used to transform points later also.

local_coordinate_coding( atoms = NA, initial_dictionary = NA, input_model = NA, lambda = NA, max_iterations = NA, normalize = FALSE, seed = NA, test = NA, tolerance = NA, training = NA, verbose = FALSE )

`atoms` |
Number of atoms in the dictionary. Default value "0" (integer). |

`initial_dictionary` |
Optional initial dictionary (numeric matrix). |

`input_model` |
Input LCC model (LocalCoordinateCoding). |

`lambda` |
Weighted l1-norm regularization parameter. Default value "0" (numeric). |

`max_iterations` |
Maximum number of iterations for LCC (0 indicates no limit). Default value "0" (integer). |

`normalize` |
If set, the input data matrix will be normalized before coding. Default value "FALSE" (logical). |

`seed` |
Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer). |

`test` |
Test points to encode (numeric matrix). |

`tolerance` |
Tolerance for objective function. Default value "0.01" (numeric). |

`training` |
Matrix of training data (X) (numeric matrix). |

`verbose` |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical). |

An implementation of Local Coordinate Coding (LCC), which codes data that approximately lives on a manifold using a variation of l1-norm regularized sparse coding. Given a dense data matrix X with n points and d dimensions, LCC seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a coding matrix Z with n points in k dimensions. Because of the regularization method used, the atoms in D should lie close to the manifold on which the data points lie.

The original data matrix X can then be reconstructed as D * Z. Therefore, this program finds a representation of each point in X as a sparse linear combination of atoms in the dictionary D.

The coding is found with an algorithm which alternates between a dictionary step, which updates the dictionary D, and a coding step, which updates the coding matrix Z.

To run this program, the input matrix X must be specified (with -i), along with the number of atoms in the dictionary (-k). An initial dictionary may also be specified with the "initial_dictionary" parameter. The l1-norm regularization parameter is specified with the "lambda" parameter.

A list with several components:

`codes` |
Output codes matrix (numeric matrix). |

`dictionary` |
Output dictionary matrix (numeric matrix). |

`output_model` |
Output for trained LCC model (LocalCoordinateCoding). |

mlpack developers

# For example, to run LCC on the dataset "data" using 200 atoms and an # l1-regularization parameter of 0.1, saving the dictionary "dictionary" and # the codes into "codes", use ## Not run: output <- local_coordinate_coding(training=data, atoms=200, lambda=0.1) dict <- output$dictionary codes <- output$codes ## End(Not run) # The maximum number of iterations may be specified with the "max_iterations" # parameter. Optionally, the input data matrix X can be normalized before # coding with the "normalize" parameter. # # An LCC model may be saved using the "output_model" output parameter. Then, # to encode new points from the dataset "points" with the previously saved # model "lcc_model", saving the new codes to "new_codes", the following # command can be used: ## Not run: output <- local_coordinate_coding(input_model=lcc_model, test=points) new_codes <- output$codes ## End(Not run)

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